Text Analytics, also known as Text Mining, is a technique used to derive insights from text data. The field has picked up some traction in review and customer analysis, and provides businesses with deeper insights regarding their customers. Everyone who has used an e-commerce site understands how consumer reviews, or lack thereof, can have a direct impact on new consumers, and thus, the business.
Communication is of paramount importance in every aspect of our lives. Data science is no exception. However, the additional complexity that Data Scientists live with is that they are required to closely interact with diverse groups of people even within a single project lifecycle. To put things into perspective, see the diagram below that captures the project lifecycle of a typical analytics project as per the internationally accepted CRISP-DM framework, and some examples of the various type of people involved at each stage.
Are you wondering about the hype around Data Science? Data says that it’s not a hype anymore. Glassdoor released its Report of 50 Best jobs in America in 2017. With a job score of 4.8 out of 5, a job satisfaction score of 4.4 out of 5, and a median base salary of $110,000, Data Scientist jobs came in first for the second year in a row.
IBM predicts demand for Data Scientists will soar 28% by 2020.
Apache Kafka is a distributed streaming platform for publishing and subscribing, storing, and processing streaming data at scale and in real-time. It has become an awesome tool for a durable system of data collection platforms.
The new release (Kafka 1.0.0) provides the following enhancements:
“If you’re trying to extract useful information from an ever-increasing inflow of data, you’ll likely find visualization useful – whether it’s to show patterns or trends with graphics instead of mountains of text, or to try to explain complex issues to a nontechnical audience.” So writes InfoWorld’s Sharon Machlis.